Abstract
Reconstructing cardiac motion from cine CMR sequences is critical for diagnosis, prediction, and intervention. Existing methods rely on complete CMR stacks to infer full heart motion, limiting their utility in intra-procedural scenarios where only sparse observations are available. We present TetHeart, the first end-to-end framework that unifies full 4D multi-structure heart mesh recovery from both offline full-stack acquisitions and intra-procedural sparse-slice observations. Our method leverages deep deformable tetrahedra, an explicit-implicit hybrid representation, to capture shape and motion in a coherent space shared across cardiac structures. It is initialized from high-quality pre-procedural or offline-acquired full stacks to build detailed, patient-specific heart meshes, which can then be updated using whatever slices are available, from full stacks down to a single slice. We further incorporate several key innovations: (i) an attentive mechanism for slice-adaptive 2D-3D feature assembly that dynamically integrates information from arbitrary numbers of slices at any position, combined with a distillation strategy from full-slice to sparse-slice settings to ensure accurate reconstruction under extreme sparsity; and (ii) a two-stage weakly supervised motion learning scheme requiring only keyframe (e.g., ED and ES) annotations. Trained and validated on three large public datasets and externally evaluated zero-shot on additional private interventional and public CMR datasets, TetHeart achieves state-of-the-art accuracy and strong generalization in both pre- and intra-procedural settings.
Abstract (translated)
从电影式CMR序列重建心脏运动对于诊断、预测和干预至关重要。现有的方法依赖于完整的CMR数据栈来推断完整的心脏运动，这限制了它们在只有稀疏观测数据可用的术中场景中的实用性。我们提出了TetHeart，这是第一个端到端框架，它统一了从离线全层获取和术中稀疏切片观察中恢复完整4D多结构心脏网格的任务。 我们的方法利用深度可变形四面体（一种显式隐式混合表示）来捕捉跨越心脏各结构的一致空间中的形状和运动。它通过高质量的术前或离线获取的全栈数据初始化，构建详细的、个性化的患者心脏网格，并可以使用任何可用切片进行更新，从全层到单个切片均可。 我们进一步融入了几个关键创新： (i) 一种注意力机制用于适应性的2D-3D特征组装，在任意位置动态整合来自任意数量切片的信息，并结合由全切片环境到稀疏切片场景的蒸馏策略，以确保在极端稀疏条件下准确重建。 (ii) 一个两阶段弱监督运动学习方案仅需关键帧（如早期舒张期和晚期收缩期）注释。 TetHeart在三个大型公共数据集上进行训练和验证，并在额外的私人介入和公开CMR数据集上进行零样本外部评估，它在术前和术中设置下均实现了最先进的准确性和强大的泛化能力。
URL
https://arxiv.org/abs/2509.12090
